A Simple Model for Predicting the Tropical Cyclone Radius of Maximum Wind from Outer Size

Abstract

Abstract The radius of maximum wind (Rmax) in a tropical cyclone governs the footprint of hazards, including damaging wind, surge, and rainfall. However, Rmax is an inconstant quantity that is difficult to observe directly and is poorly resolved in reanalyses and climate models. In contrast, outer wind radii are much less sensitive to such issues. Here we present a simple empirical model for predicting Rmax from the radius of 34-kt (1 kt ≈ 0.51 m s−1) wind (R17.5 ms). The model only requires as input quantities that are routinely estimated operationally: maximum wind speed, R17.5 ms, and latitude. The form of the empirical model takes advantage of our physical understanding of tropical cyclone radial structure and is trained on the Extended Best Track database from the North Atlantic 2004–20. Results are similar for the TC-OBS database. The physics reduces the relationship between the two radii to a dependence on two physical parameters, while the observational data enables an optimal estimate of the quantitative dependence on those parameters. The model performs substantially better than existing operational methods for estimating Rmax. The model reproduces the observed statistical increase in Rmax with latitude and demonstrates that this increase is driven by the increase in R17.5 ms with latitude. Overall, the model offers a simple and fast first-order prediction of Rmax that can be used operationally and in risk models. Significance Statement If we can better predict the area of strong winds in a tropical cyclone, we can better prepare for its potential impacts. This work develops a simple model to predict the radius where the strongest winds in a tropical cyclone are located. The model is simple and fast and more accurate than existing models, and it also helps us to understand what causes this radius to vary in time, from storm to storm, and at different latitudes. It can be used in both operational forecasting and models of tropical cyclone hazard risk.